Detection and Segmentation of Brain Tumors from MRI Using U-Nets

Kotowski, Krzysztof; Nalepa, Jakub; Dudzik, Wojciech

doi:10.1007/978-3-030-46643-5_17

Cited by 9 publications

(11 citation statements)

References 37 publications

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“…Zhang, et al 32 developed a DDU‐Nets for glioma segmentation. Kotowski, et al 33 proposed a cascaded U‐Net architecture to perform detection and segmentation of brain tumors from MR scans. Naceur, et al 34 used deep transfer learning to perform tumor segmentation.…”

Section: Methodsmentioning

confidence: 99%

A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Chen

Ban

et al. 2021

Medical Physics

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Purpose To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimension radiomic features extracted from four sequences of MRIs and 128‐dimension high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including nonenhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross validation, the model was independently validated using the rest 35 patients from the same database. Results The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients.

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Section: Methodsmentioning

confidence: 99%

A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Chen

Ban

et al. 2021

Medical Physics

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“…We compared our results with the other six state‐of‐the‐art approaches 29‐34 . Cheng et al 29 use Spatial‐channel relation learning for brain tumor segmentation.…”

Section: Methodsmentioning

confidence: 99%

“…Zhang et al 32 developed a DDU‐Nets for glioma segmentation. Kotowski et al 33 proposed a cascaded U‐Net architecture to perform detection and segmentation of brain tumors from MR scans. Naceur et al 34 use deep transfer learning to perform tumor segmentation.…”

Section: Methodsmentioning

confidence: 99%

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Withdrawn: A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Chen

Ban

et al. 2021

Medical Physics

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Purpose To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimensions radiomics features extracted from four sequences of MRIs and 128‐dimensions high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including non‐enhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross‐validation, and the model was independently validated using the rest 35 patients from the same database. Results The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients.

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“…The first UNet was used to segment the entire tumor area, and the second UNet was used to segment it into three predefined classes. Kotowski et al [27] uses the same approach by detecting all tumor areas followed by multiclass classification of pixels that have been detected as tumors. This 2D processing approach for 3D images has the advantage of using minimal GPU memory footprint but eliminating voxel connectivity information in 3D space.…”

Section: Related Workmentioning

confidence: 99%

SDA-UNET2.5D: Shallow Dilated with Attention Unet2.5D for Brain Tumor Segmentation

2022

IJIES

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Many studies have been carried out to segmentation brain tumors on 3D Magnetic Resonance Imaging (MRI) images with 3D or 2D approaches. The 3D approach pays attention to the interrelationships between slices in a 3D image. However, this requires high resources, while the 2D approach requires lower resources but ignores the voxel relationship in 3D space. The 2.5D approach seeks to combine the lightness of the 2D approach and the voxel interconnection of the 3D approach. This article proposes SDA-UNet2.5D, a shallow UNet 2.5D architecture that pays attention to the interconnectedness of 3D images by involving five slices to get one slice of segmentation prediction results. The architecture is trained using the Brain Tumor Segmentation (BraTS) 2018, 2019, and 2020 datasets. Compared to other architectures, this proposed architecture has a high segmentation speed with 4.05-4.24 seconds to segment one patient data. Online validation resulted in superior average dice

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Detection and Segmentation of Brain Tumors from MRI Using U-Nets

Cited by 9 publications

References 37 publications

A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Withdrawn: A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

SDA-UNET2.5D: Shallow Dilated with Attention Unet2.5D for Brain Tumor Segmentation

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